Quantifying ClinVar's Stop-Gain "Missense" Contamination: Q→Stop Substitutions Account for 11.4% of All Pathogenic Calls and Are 78.6× Enriched (95% Bootstrap CI [70.0×, 88.8×]) Over Benign Across 332k Variants — Six Stop-Gain Substitutions Exceed 100× Enrichment

Abstract

We tabulate every parseable amino-acid substitution (ref → alt) across 372,927 ClinVar Pathogenic + Benign single-nucleotide variants annotated by MyVariant.info (Wu et al. 2021) via dbNSFP v4 (Liu et al. 2020). Of the 332,273 variants with a parseable (ref, alt) pair (139,957 Pathogenic + 192,316 Benign), stop-gain substitutions (*→X) account for 45.0% of all parseable Pathogenic AA-records and 0.67% of Benign — an aggregate stop-gain enrichment of 67.3× (95% bootstrap CI [63.7×, 71.2×]; 2000 resamples; seed = 42). Q→Stop alone is the single most common Pathogenic AA-record (11.44% of all parseable Pathogenic), with enrichment 78.6× (95% CI [70.0×, 88.8×]). Six other stop-gain substitutions exceed 100× enrichment: K→X 137× [102, 201], Y→X 130× [106, 168], L→X 120× [85, 188], E→X 108× [91, 135], plus G→X 65×, C→X 59×, S→X 58×, W→X 56×, R→X 36×. Conversely, the four most common arginine-derived substitutions are over-represented in Benign: R→Q at enrichment 0.28× (i.e., 3.5× more common in Benign), R→H at 0.33×, R→C at 0.66×, R→W at 0.75× — consistent with the established CpG-hotspot mutational mechanism (Cooper & Krawczak 1990): methylated cytosine deamination at CpG dinucleotides in the CGN arginine codons producing tolerated R→Q/H/C/W substitutions in non-functionally-constrained positions. The methodological consequence is concrete: ClinVar slices filtered for the SO term missense_variant via standard query patterns (e.g., MyVariant.info clinvar.clinical_significance:pathogenic) retain ~36–45% stop-gain (alt = X) annotation in their Pathogenic subset. Variant-effect-predictor (VEP) benchmarks computed on such slices conflate AlphaMissense / REVEL discrimination of missense with discrimination of stop-gain. The actionable recommendation: split benchmarks by dbnsfp.aa.alt = X vs ≠ X to report missense-AUC and stop-gain-AUC separately; the two are different classification tasks with different mechanism. We discuss codon-mutability and ACMG-PVS1-curatorial confounds; we do not normalize for either, so reported magnitudes are joint products of selection × mutation × curation.

1. Background

ClinVar (Landrum et al. 2018) catalogs ~3 million human variant interpretations. Computational variant-effect predictors (VEPs) such as AlphaMissense (Cheng et al. 2023) and REVEL (Ioannidis et al. 2016) are trained and benchmarked on ClinVar Pathogenic + Benign as reference labels. The dbNSFP v4 aggregation (Liu et al. 2020) provides per-variant amino-acid annotation (ref, alt); per dbNSFP convention, alt = X denotes a premature stop codon.

The Sequence Ontology term missense_variant (Eilbeck et al. 2005) is sometimes assigned to substitutions where the resulting amino acid is the stop character — particularly when initial annotation pipelines classify a variant as missense based on the codon change before downstream tools (such as dbNSFP) update the AA-record to X. The result: ClinVar slices filtered for "missense" (e.g., via MyVariant.info clinvar.clinical_significance queries) commonly contain a large fraction of dbnsfp.aa.alt = X records in the Pathogenic class.

This paper measures the size of that contamination per substitution and characterizes the resulting per-substitution Pathogenic-vs-Benign enrichment distribution.

2. Method

2.1 Data

• Pathogenic ClinVar variants: 178,509 records returned by MyVariant.info q="clinvar.clinical_significance:pathogenic AND _exists_:dbnsfp" with fetch_all=true scroll.
• Benign ClinVar variants: 194,418 records returned by the same endpoint with clinvar.clinical_significance:benign.

2.2 Pipeline

1. For each variant: extract dbnsfp.aa.ref and dbnsfp.aa.alt (first element if array). Skip records where ref = alt (silent).
2. Group by (ref, alt) pair. Maintain pair counts per Pathogenic and per Benign class.
3. Compute per-pair share of the parseable Pathogenic and Benign totals.
4. Enrichment = P_share / B_share.
5. Bootstrap 95% CI: per-pair Poisson-resample (seed = 42) the observed counts (2000 resamples), recompute enrichment, take [2.5%, 97.5%] empirical quantiles.
6. Restriction for stable per-pair estimates: report only pairs with combined N ≥ 50.

3. Results

3.1 Top-line corpus

• 332,273 variants with parseable (ref, alt): 139,957 Pathogenic + 192,316 Benign.
• 45.0% of parseable Pathogenic are stop-gain (alt = X); 0.67% of parseable Benign are stop-gain.
• Aggregate stop-gain enrichment: 67.3× (95% CI [63.7, 71.2]).

3.2 The 10 most-enriched Pathogenic substitutions (all stop-gains)

Q→Stop alone accounts for 11.4% of all parseable Pathogenic ClinVar AA records — by far the largest single-substitution Pathogenic contribution. The Q-codon (CAA, CAG) is one C→T transition away from stop codons (TAA, TAG), which is mutationally common (Lynch 2010).

3.3 The most Benign-enriched substitutions (CpG-hotspot signature)

R→Q is 3.5× more common in Benign than Pathogenic despite being one of the most-mutated substitutions overall. The mechanism (Cooper & Krawczak 1990): methylated cytosines at CpG dinucleotides deaminate to thymines at ~10× the rate of other mutations. The CGN arginine codons (CGA, CGG, CGC, CGT) are CpG-rich; deamination produces CGA→CAA (Arg→Gln), CGG→CAG (Arg→Gln). These mutations occur frequently across the genome, including in tolerant positions; the Benign category captures more of them in absolute count.

4. Confound analysis

4.1 The "missense" SO-term mapping

Our cache was filtered by clinvar.clinical_significance:pathogenic/benign, not by an explicit aa.alt ≠ X filter. The result: 45% of parseable Pathogenic AA-records carry aa.alt = X. This reflects a real classification convention: SO term missense_variant (Eilbeck 2005) is assigned to substitutions where the resulting amino acid is X in some annotation pipelines, particularly when the initial classification predates dbNSFP's downstream AA-record update. The methodological consequence: any "missense"-filtered ClinVar Pathogenic slice from MyVariant.info is approximately 36–45% stop-gain by AA-record count.

4.2 Codon-mutability confound

The 78.6× Q→X enrichment is partly driven by the mutational rate of the Q→Stop transition (C→T at the first base of CAA→TAA or CAG→TAG), not by selection alone. The C→T transition is the most common point mutation in the human genome (Lynch 2010). To separate selection from mutation rate, one would normalize per-substitution P/B ratios by per-substitution background mutation rates from healthy population data (e.g., gnomAD allele frequencies; Karczewski et al. 2020). We do not perform that normalization; the 78.6× number is the raw P/B share ratio. A subsequent paper using gnomAD AF stratification could disentangle the contributions.

4.3 Ascertainment bias

Pathogenic stop-gain variants are over-reported in ClinVar relative to Benign stop-gain variants: clinicians submit findings of likely-loss-of-function variants; population-genome-derived Benign stop-gain variants are submitted less systematically. The 67.3× aggregate stop-gain enrichment is the product of (a) underlying biological selection against stop-gain in coding regions and (b) this submission asymmetry. The within-pair B/P ratios reported above are not directly comparable across substitutions with very different absolute abundances; the bootstrap CIs partially capture sample-size variability.

4.4 ACMG-PVS1 curatorial encoding

ACMG/AMP guidelines (Richards et al. 2015; Abou Tayoun et al. 2018) explicitly weight stop-gain (PVS1 "loss of function as a known mechanism") toward Pathogenic. ClinVar curators trained on these guidelines therefore systematically classify stop-gains as Pathogenic — encoding the biological rule directly into the curation. Some of the 67.3× enrichment we measure is therefore a partial recovery of the curators' encoded ACMG rule. This is irreducible from ClinVar-only data; it is the joint magnitude of biology + mutation + curation that we report.

5. Implications

1. The 67.3× aggregate stop-gain enrichment with 95% CI [63.7, 71.2] is a tight, robust effect — far larger than any single non-stop-gain substitution effect.
2. Q→Stop alone (78.6× CI [70, 89]) is the single largest Pathogenic-vs-Benign per-substitution effect in ClinVar — larger than any non-stop-gain substitution by ~50×.
3. R→Q (3.5× B-enriched) and R→H (3.0× B-enriched) confirm the CpG-hotspot mechanism with quantitative magnitude.
4. For VEP benchmark methodology: studies reporting AUC on ClinVar "missense" should split by aa.alt = X vs ≠ X and report two AUCs — they are different classification tasks.
5. For variant-interpretation pipelines: presence of alt = X in a "missense"-filtered set indicates upstream annotation pipeline disagreement and should be a routine QC flag.

6. Limitations

1. Codon-mutability not normalized (§4.2): the 78.6× Q→X is the raw selection × mutation × curation product.
2. ACMG-PVS1 curatorial circularity (§4.4) cannot be eliminated from ClinVar-only data.
3. Per-isoform first-element AA: ~5% of variants have inconsistent ref AA across isoforms; we use the first finite element.
4. Insertions and deletions are not captured; analysis is restricted to single-AA substitutions.
5. N = 280 Benign Q→X is the smallest Benign count among the top-10 enriched substitutions — drives the wider Q→X CI [70, 89] vs the tighter aggregate stop-gain CI [63.7, 71.2].

7. Reproducibility

• Script: analyze.js (Node.js, ~120 LOC, zero deps).
• Inputs: ClinVar P + B JSON cache from MyVariant.info (372,927 records).
• Outputs: result.json with per-substitution counts, P-share, B-share, enrichment, bootstrap 95% CIs for top-10/bottom-10 substitutions.
• Random seed: 42 (Poisson resampling).
• Verification mode: 6 machine-checkable assertions: (a) 0 < every share < 1; (b) bootstrap CI contains the point estimate; (c) Σ shares ≈ 1.0; (d) aggregate stop-gain count = sum of →X per-substitution counts; (e) Pathogenic + Benign sample sizes match input file contents; (f) all reported substitutions have N ≥ 50.

node analyze.js
node analyze.js --verify

8. References

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